ANALISIS PENGAWASAN BERBASIS DATA ANALYTICS DALAM MENINGKATKAN AKUNTABILITAS EVALUASI PENDIDIKAN DI ERA TRANSFORMASI DIGITAL MTS. AL FALAH PUTRI WULUHAN
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Abstract
This research aims to analyze the effectiveness of implementing a data analytics-based digital dashboard in improving the transparency, accuracy, and responsiveness of education supervision. The research uses a qualitative method with a case study design combined with a systematic review thru the PRISMA framework. Data collection techniques include field observation, in-depth interviews, performance documentation, and a search of reputable national and international articles. Data analysis techniques used a thematic model, referencing Thematic Analysis by Braun and Clarke, employing a systematic coding process to inductively and deductively identify and interpret key patterns in empirical data, resulting in structured, objective, and contextual interpretations. Data validity was tested using source, technique, and theory triangulation to ensure the validity and reliability of the findings. The research results indicate that utilizing data analytics thru digital dashboards can strengthen performance indicator mapping, provide early detection of quality deviations, present objective visualizations of achievements, and predict risks of declining educational quality, while simultaneously addressing classic problems such as delayed reports, low validity of field findings, and weak coordination between educational units. The implementation of a data analytics-based digital dashboard allows for the real-time, objective, and measurable integration of teacher performance data and learning achievement. The positive impacts include increased transparency, accuracy of evaluation, and speed of supervisory decision-making. Obstacles such as digital literacy limitations and data fragmentation are overcome thru indicator standardization, continuous training, and the integration of systems based on institutional policies.
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This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
References
Anidjar, L., Packin, N. G., & Panezi, A. (2023). The matrix of privacy: data infrastructure in the AI-Powered Metaverse. Harv. L. & Pol’y Rev., 18, 59.
Anugraeni, A. P. (2024). Pembangunan Dashboard Business Intelligence Visualisasi Data Akademik Pada MTSN 3 Malang. Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer, 8(8).
Braun, V., & Clarke, V. (2021). One size fits all? What counts as quality practice in (reflexive) thematic analysis? Qualitative Research in Psychology, 18(3), 328–352.
Chen, L., Ifenthaler, D., & Yau, J. Y.-K. (2022). The Effectiveness of Virtual Team Learning and Its Potential Factors in Entrepreneurship Education Courses. CSEDU (1), 250–256.
di Cicco, M. R., Masiello, A., Spagnuolo, A., Vetromile, C., Borea, L., Giannella, G., Iovinella, M., & Lubritto, C. (2021). Real-time monitoring and static data analysis to assess energetic and environmental performances in the wastewater sector: A case study. Energies, 14(21), 6948.
Divjak, B., Svetec, B., & Horvat, D. (2023). Learning analytics dashboards: What do students actually ask for? LAK23: 13th International Learning Analytics and Knowledge Conference, 44–56.
Fahrudin, T. M., Swari, M. H. P., Irawan, R. N., Ansori, N. A. V., & Putri, N. R. A. (2021). Optimalisasi dashboard analytics dan iklanku shopee sebagai strategi penjualan alat dan perlengkapan dapur secara online. Transform. J. Pengabdi. Masy, 17(2), 136–150.
Hadjigeorgiou, E., Clark, B., Simpson, E., Coles, D., Comber, R., Fischer, A. R. H., Meijer, N., Marvin, H. J. P., & Frewer, L. J. (2022). A systematic review into expert knowledge elicitation methods for emerging food and feed risk identification. Food Control, 136, 108848.
Hasnine, M. N., Nguyen, H. T., Tran, T. T. T., Bui, H. T. T., Akçapınar, G., & Ueda, H. (2023). A real-time learning analytics dashboard for automatic detection of online learners’ affective states. Sensors, 23(9), 4243.
Ifenthaler, D., & Yau, J. Y.-K. (2022). Analytics for supporting teaching success in higher education: A systematic review. 2022 IEEE Global Engineering Education Conference (EDUCON), 1721–1727.
Kew, S. N., & Tasir, Z. (2022). Learning analytics in online learning environment: A systematic review on the focuses and the types of student-related analytics data. Technology, Knowledge and Learning, 27(2), 405–427.
Kitchin, R. (2021). The Data Revolution: A critical analysis of big data, open data and data infrastructures.
Mandinach, E. B., & Schildkamp, K. (2021). Misconceptions about data-based decision making in education: An exploration of the literature. Studies in Educational Evaluation, 69, 100842.
Masiello, I., Mohseni, Z., Palma, F., Nordmark, S., Augustsson, H., & Rundquist, R. (2024). A current overview of the use of learning analytics dashboards. Education Sciences, 14(1), 82.
Necherda, V. B. (2021). Case-study technology in the formation of a socially successful pupil’s personality.
Pei, B., Cheng, Y., Ambrose, A., Dziadula, E., Xing, W., & Lu, J. (2024). LearningViz: a dashboard for visualizing, analyzing and closing learning performance gaps—a case study approach. Smart Learning Environments, 11(1), 56.
Poth, C. N., & Shannon-Baker, P. (2022). State of the methods: Leveraging design possibilities of qualitatively oriented mixed methods research. International Journal of Qualitative Methods, 21, 16094069221115302.
Prinsloo, P., & Kaliisa, R. (2022). Learning analytics on the African continent: an emerging research focus and practice. Journal of Learning Analytics, 9(2), 218–235.
Ramaswami, G., Susnjak, T., Mathrani, A., & Umer, R. (2023). Use of predictive analytics within learning analytics dashboards: A review of case studies. Technology, Knowledge and Learning, 28(3), 959–980.
Rivai, D. N., Ramadhan, T. I., & Supriatman, A. (2024). Implementasi Data Analytics Guna Optimalisasi Kegiatan Penerimaan Santri Baru (PSB) Berbasis Dashboard. E-Jurnal JUSITI (Jurnal Sistem Informasi Dan Teknologi Informasi), 13(2), 155–168.
Rolansa, F. (2021). Pengembangan interaktif dashboard kemahasiswaan di program studi teknik informatika dengan teknologi big data. Jurnal Pendidikan Informatika Dan Sains, 10(2), 110–118.
Salas-Pilco, S. Z., Xiao, K., & Hu, X. (2023). Correction: Salas-Pilco et al. Artificial Intelligence and Learning Analytics in Teacher Education: A Systematic Review. Educ. Sci. 2022, 12, 569. Education Sciences, 13(9).
Susnjak, T., Ramaswami, G. S., & Mathrani, A. (2022). Learning analytics dashboard: a tool for providing actionable insights to learners. International Journal of Educational Technology in Higher Education, 19(1), 12.
Tony, T., Handhayani, T., & Dayanti, A. P. (2024). Dashboard Design to Monitor the Number of Students of the Faculty of Information Technology, Tarumanagara University. Sebatik, 28(2), 396–402.
Valle, N., Antonenko, P., Dawson, K., & Huggins‐Manley, A. C. (2021). Staying on target: A systematic literature review on learner‐facing learning analytics dashboards. British Journal of Educational Technology, 52(4), 1724–1748.
Verbert, K., Ochoa, X., De Croon, R., Dourado, R. A., & De Laet, T. (2020). Learning analytics dashboards: The past, the present and the future. Proceedings of the Tenth International Conference on Learning Analytics & Knowledge, 35–40.
Visscher, A. J. (2021). On the value of data-based decision making in education: The evidence from six intervention studies. Studies in Educational Evaluation, 69, 100899.
Williamson, B. (2021). Making markets through digital platforms: Pearson, edu-business, and the (e) valuation of higher education. Critical Studies in Education, 62(1), 50–66.
Yumarlin, M. Z., Bororing, J. E., Rahayu, S., & Ramadhani, T. A. (2022). Aplikasi Dashboard Visualisasi Data Calon Mahasiswa Baru mengunakan Metabase. Edumatic: Jurnal Pendidikan Informatika, 6(1), 116–125.
Zuliati, S. D., Utami, L. W. C., Perta, H., Lutfiana, A. F., Riana, H., Rosyad, S., Azwardi, M., Prihandini, T. F., & Arbi, M. (2025). TRANSFORMASI DAN INOVASI PENDIDIKAN. NABA EDUKASI INDONESIA.
Main Article Content
Abstract
This research aims to analyze the effectiveness of implementing a data analytics-based digital dashboard in improving the transparency, accuracy, and responsiveness of education supervision. The research uses a qualitative method with a case study design combined with a systematic review thru the PRISMA framework. Data collection techniques include field observation, in-depth interviews, performance documentation, and a search of reputable national and international articles. Data analysis techniques used a thematic model, referencing Thematic Analysis by Braun and Clarke, employing a systematic coding process to inductively and deductively identify and interpret key patterns in empirical data, resulting in structured, objective, and contextual interpretations. Data validity was tested using source, technique, and theory triangulation to ensure the validity and reliability of the findings. The research results indicate that utilizing data analytics thru digital dashboards can strengthen performance indicator mapping, provide early detection of quality deviations, present objective visualizations of achievements, and predict risks of declining educational quality, while simultaneously addressing classic problems such as delayed reports, low validity of field findings, and weak coordination between educational units. The implementation of a data analytics-based digital dashboard allows for the real-time, objective, and measurable integration of teacher performance data and learning achievement. The positive impacts include increased transparency, accuracy of evaluation, and speed of supervisory decision-making. Obstacles such as digital literacy limitations and data fragmentation are overcome thru indicator standardization, continuous training, and the integration of systems based on institutional policies.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
References
Anidjar, L., Packin, N. G., & Panezi, A. (2023). The matrix of privacy: data infrastructure in the AI-Powered Metaverse. Harv. L. & Pol’y Rev., 18, 59.
Anugraeni, A. P. (2024). Pembangunan Dashboard Business Intelligence Visualisasi Data Akademik Pada MTSN 3 Malang. Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer, 8(8).
Braun, V., & Clarke, V. (2021). One size fits all? What counts as quality practice in (reflexive) thematic analysis? Qualitative Research in Psychology, 18(3), 328–352.
Chen, L., Ifenthaler, D., & Yau, J. Y.-K. (2022). The Effectiveness of Virtual Team Learning and Its Potential Factors in Entrepreneurship Education Courses. CSEDU (1), 250–256.
di Cicco, M. R., Masiello, A., Spagnuolo, A., Vetromile, C., Borea, L., Giannella, G., Iovinella, M., & Lubritto, C. (2021). Real-time monitoring and static data analysis to assess energetic and environmental performances in the wastewater sector: A case study. Energies, 14(21), 6948.
Divjak, B., Svetec, B., & Horvat, D. (2023). Learning analytics dashboards: What do students actually ask for? LAK23: 13th International Learning Analytics and Knowledge Conference, 44–56.
Fahrudin, T. M., Swari, M. H. P., Irawan, R. N., Ansori, N. A. V., & Putri, N. R. A. (2021). Optimalisasi dashboard analytics dan iklanku shopee sebagai strategi penjualan alat dan perlengkapan dapur secara online. Transform. J. Pengabdi. Masy, 17(2), 136–150.
Hadjigeorgiou, E., Clark, B., Simpson, E., Coles, D., Comber, R., Fischer, A. R. H., Meijer, N., Marvin, H. J. P., & Frewer, L. J. (2022). A systematic review into expert knowledge elicitation methods for emerging food and feed risk identification. Food Control, 136, 108848.
Hasnine, M. N., Nguyen, H. T., Tran, T. T. T., Bui, H. T. T., Akçapınar, G., & Ueda, H. (2023). A real-time learning analytics dashboard for automatic detection of online learners’ affective states. Sensors, 23(9), 4243.
Ifenthaler, D., & Yau, J. Y.-K. (2022). Analytics for supporting teaching success in higher education: A systematic review. 2022 IEEE Global Engineering Education Conference (EDUCON), 1721–1727.
Kew, S. N., & Tasir, Z. (2022). Learning analytics in online learning environment: A systematic review on the focuses and the types of student-related analytics data. Technology, Knowledge and Learning, 27(2), 405–427.
Kitchin, R. (2021). The Data Revolution: A critical analysis of big data, open data and data infrastructures.
Mandinach, E. B., & Schildkamp, K. (2021). Misconceptions about data-based decision making in education: An exploration of the literature. Studies in Educational Evaluation, 69, 100842.
Masiello, I., Mohseni, Z., Palma, F., Nordmark, S., Augustsson, H., & Rundquist, R. (2024). A current overview of the use of learning analytics dashboards. Education Sciences, 14(1), 82.
Necherda, V. B. (2021). Case-study technology in the formation of a socially successful pupil’s personality.
Pei, B., Cheng, Y., Ambrose, A., Dziadula, E., Xing, W., & Lu, J. (2024). LearningViz: a dashboard for visualizing, analyzing and closing learning performance gaps—a case study approach. Smart Learning Environments, 11(1), 56.
Poth, C. N., & Shannon-Baker, P. (2022). State of the methods: Leveraging design possibilities of qualitatively oriented mixed methods research. International Journal of Qualitative Methods, 21, 16094069221115302.
Prinsloo, P., & Kaliisa, R. (2022). Learning analytics on the African continent: an emerging research focus and practice. Journal of Learning Analytics, 9(2), 218–235.
Ramaswami, G., Susnjak, T., Mathrani, A., & Umer, R. (2023). Use of predictive analytics within learning analytics dashboards: A review of case studies. Technology, Knowledge and Learning, 28(3), 959–980.
Rivai, D. N., Ramadhan, T. I., & Supriatman, A. (2024). Implementasi Data Analytics Guna Optimalisasi Kegiatan Penerimaan Santri Baru (PSB) Berbasis Dashboard. E-Jurnal JUSITI (Jurnal Sistem Informasi Dan Teknologi Informasi), 13(2), 155–168.
Rolansa, F. (2021). Pengembangan interaktif dashboard kemahasiswaan di program studi teknik informatika dengan teknologi big data. Jurnal Pendidikan Informatika Dan Sains, 10(2), 110–118.
Salas-Pilco, S. Z., Xiao, K., & Hu, X. (2023). Correction: Salas-Pilco et al. Artificial Intelligence and Learning Analytics in Teacher Education: A Systematic Review. Educ. Sci. 2022, 12, 569. Education Sciences, 13(9).
Susnjak, T., Ramaswami, G. S., & Mathrani, A. (2022). Learning analytics dashboard: a tool for providing actionable insights to learners. International Journal of Educational Technology in Higher Education, 19(1), 12.
Tony, T., Handhayani, T., & Dayanti, A. P. (2024). Dashboard Design to Monitor the Number of Students of the Faculty of Information Technology, Tarumanagara University. Sebatik, 28(2), 396–402.
Valle, N., Antonenko, P., Dawson, K., & Huggins‐Manley, A. C. (2021). Staying on target: A systematic literature review on learner‐facing learning analytics dashboards. British Journal of Educational Technology, 52(4), 1724–1748.
Verbert, K., Ochoa, X., De Croon, R., Dourado, R. A., & De Laet, T. (2020). Learning analytics dashboards: The past, the present and the future. Proceedings of the Tenth International Conference on Learning Analytics & Knowledge, 35–40.
Visscher, A. J. (2021). On the value of data-based decision making in education: The evidence from six intervention studies. Studies in Educational Evaluation, 69, 100899.
Williamson, B. (2021). Making markets through digital platforms: Pearson, edu-business, and the (e) valuation of higher education. Critical Studies in Education, 62(1), 50–66.
Yumarlin, M. Z., Bororing, J. E., Rahayu, S., & Ramadhani, T. A. (2022). Aplikasi Dashboard Visualisasi Data Calon Mahasiswa Baru mengunakan Metabase. Edumatic: Jurnal Pendidikan Informatika, 6(1), 116–125.
Zuliati, S. D., Utami, L. W. C., Perta, H., Lutfiana, A. F., Riana, H., Rosyad, S., Azwardi, M., Prihandini, T. F., & Arbi, M. (2025). TRANSFORMASI DAN INOVASI PENDIDIKAN. NABA EDUKASI INDONESIA.